Nezha: Breaking Multi-Rail Network Barriers for Distributed DNN Training

In distributed deep learning, communication remains a critical bottleneck. While modern hardware advances rapidly, over 60 percent of production HPC systems still rely on legacy infrastructure (V100 GPUs, multi-plane Ethernet/InfiniBand), necessitating communication optimization without hardware upgrades. Existing approaches face three key limitations: (1) static single-rail binding underutilizes multi-rail bandwidth, (2) protocol heterogeneity (TCP-RDMA coexistence) causes synchronization delays, and (3) mainstream libraries (NCCL/MPI) lack cross-protocol coordination. We present Nezha, the first protocol-agnostic system for multi-rail networks. Our contributions include: (1) Hardware-agnostic cross-protocol coordination: A unified abstraction enabling seamless collaboration between in-network computing (SHARP), adaptive RDMA (GLEX), and TCP, achieving 1.7 to 4.3 times lower latency than Gloo. (2) Protocol-aware dynamic load balancing: A hybrid scheduling strategy with cold/hot start state machine for heterogeneous protocols, reducing startup latency for small payloads while enhancing throughput for large transfers. (3) Fault-tolerant multi-rail collaboration: A self-recovery mechanism that reroutes data flows within 200 milliseconds upon single-rail failures, ensuring uninterrupted training. Experiments on 8-node clusters demonstrate Nezha achieves 74 percent and 80 percent higher throughput than MPTCP in homogeneous (TCP-TCP) and heterogeneous (TCP-SHARP) networks, respectively. On 128-node supercomputers, Nezha delivers 2.36 times higher training efficiency than Gloo. By bridging modern DNN communication demands with legacy infrastructure, Nezha proves that systematic multi-rail optimization can unlock the potential of aging clusters.